GitHub repository that we used for the API call functions:

Wurzbacher, M. (2023). FAIR Checker evaluation. In FAIRness assessment tools evaluation using ORKG. GitHub. https://github.com/mwurzbacher/FAIRness-assessment-tools-evaluation-using-ORKG/blob/main/FAIR_Checker_evaluation.ipynb¶

Importing the neccessary packages¶

In [5]:
import requests
import re
import json
import pandas as pd
import numpy as np
import matplotlib.lines as mlines
from itertools import chain
from matplotlib import pyplot as plt
import os

Defining the neccessary functions for the assessment¶

In [2]:
# Making the get request to the Fair-Checker API that includes checking for all available metrics
# If the call is successful it stores the result and status code 200, else it stores None and 504
def get_all_metrics (payload):
    global count
    print(count)
    FAIR_checker_api = "https://fair-checker.france-bioinformatique.fr/api/check/metrics_all"
    try:
        response = requests.get(FAIR_checker_api, params=payload)
        print(payload)
        print(response.status_code)
        print("________________")
        count += 1
        return response.json(), response.status_code
    except:
        status = 504
        print(payload)
        print(status)
        print("________________")
        return None, status
    
    
# Transforms the result from the API call into a JSON dictionary   
def build_assessment_json(assessment):
    regex_result = re.findall("'metric': '.*?', 'score': '.*?'", assessment)
    assessment_list = []
    for result in regex_result:
        result = result.replace("\'", "\"")
        assessment_dict = json.loads("{"+result+"}")
        assessment_list.append(assessment_dict)
    return assessment_list

# Prints the most basic summary statistics about the 'Score' column
def print_summary_stats(df):
    print("Mean: " + str(df["Score"].mean()))
    print("Median: " + str(df["Score"].median()))
    print("Max: " + str(df["Score"].max()))
    print("Min: " + str(df["Score"].min()))

Cleaning the first (raw) file from data.gov.at¶

In [6]:
path = r"C:\Users\MolnarM\OneDrive - BDO Austria\Desktop\DSAI2"

os.chdir(path)
In [ ]:
input_file_path = r"./ogdmetadatendatagvat.csv"
output_file_path = r"./ogdmetadatendatagvat_cleaned.csv"

# Read the content of the file
with open(input_file_path, 'r') as file:
    content = file.read()

# Replace backslashes with forward slashes
modified_content = content.replace("\\", "/")

# Write the modified content back to a new file
with open(output_file_path, 'w') as file:
    file.write(modified_content)

Importing the "cleaned" file, removing the first redundant rows¶

In [ ]:
df = pd.read_csv(r"./ogdmetadatendatagvat_cleaned.csv", encoding= 'unicode_escape', sep=';', on_bad_lines='skip', header=5, skiprows=[6,7])
df.head(10)
In [ ]:
#defining the date (its performed on)

date = "2024-05-14"

Running the assesment loop¶

In [ ]:
count = 0
assessment_results = df['metadata_identifier'].map(lambda x: get_all_metrics(payload={'url': 'https://www.data.gv.at/katalog/dataset/' + x}))
df['FAIR_assessment'] = [aresult[0] for aresult in assessment_results]
df['assessment_status_code'] = [aresult[1] for aresult in assessment_results]
df.to_csv(r"./ogdmetadatendatagvat_assessed_"+date+".csv", encoding='utf-8')
df

Modified code to include saving after every 100 assessed file as we were losing the results because some issues that broke the loop¶

In [ ]:
import pandas as pd
import requests
import time

# Keeping track of the progress
count = 0

def get_all_metrics(payload):
    global count
    print(count)
    FAIR_checker_api = "https://fair-checker.france-bioinformatique.fr/api/check/metrics_all"
    try:
        response = requests.get(FAIR_checker_api, params=payload)
        print(payload)
        print(response.status_code)
        print("________________")
        count += 1
        return response.json(), response.status_code
    except:
        status = 504
        print(payload)
        print(status)
        print("________________")
        return None, status

def assess_and_save(df, save_interval=100, save_path=r"./ogdmetadatendatagvat_assessed_intermediate.csv"):
    # Initialize columns with None
    df['FAIR_assessment'] = None
    df['assessment_status_code'] = None

    for index, x in df['metadata_identifier'].iteritems():
        if pd.notna(x):
            result = get_all_metrics(payload={'url': 'https://www.data.gv.at/katalog/dataset/' + str(x)})
        else:
            result = (None, None)
        
        # Update the DataFrame with the result
        df.at[index, 'FAIR_assessment'] = result[0]
        df.at[index, 'assessment_status_code'] = result[1]
        
        # Save intermediate results
        if (index + 1) % save_interval == 0:
            df.to_csv(save_path, encoding='utf-8', index=False)
            print(f"Saved intermediate results at index {index + 1}")
            time.sleep(1)  # To avoid hitting API rate limits or overloading the server
    
    return df

# Perform the assessment and save intermediate results
df = assess_and_save(df)

# Save the final results
date = "20230527"  # Replace with your desired date string
output_path = r"./ogdmetadatendatagvat_assessed_" + date + ".csv"
df.to_csv(output_path, encoding='utf-8', index=False)
In [3]:
# Saving the results as an excel file as well
df = pd.read_excel(r"./assessed_excel.xlsx")

Check out what came out of it¶

In [ ]:
df['FAIR_assessment'][0]
In [10]:
# Only keeping successful assessments
df = df[df["assessment_status_code"] == 200]
df = df.drop(labels=["assessment_status_code"], axis=1).reset_index(drop=True)
# Transforming the results to JSON dicts
df["FAIR_assessment"] = df["FAIR_assessment"].map(lambda x: build_assessment_json(x))
df
Out[10]:
Unnamed: 0 local_id metadata_identifier metadata_modified Title description categorization tags maintainer license ... lineage_quality en_title_and_desc license_citation metadata_original_portal maintainer_email publisher_link publisher_email license_url terms_url FAIR_assessment
0 0 1 3E7BDC0B-6CBC-5A25-AF07-409E1EA40408 2023-07-03 Entwicklung nachhaltiger Schilferntetechniken ... NaN Umwelt Nationalparks Austria, Naturschutz, OpenDocume... NP Neusiedler See - Seewinkel Creative Commons Attribution License 3.0 ... NaN NaN NaN NaN NaN NaN NaN https://creativecommons.org/licenses/by/3.0/ NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
1 1 2 d7e2c489-c233-4458-9d98-a2048a73a43e 2023-07-02 Voranschlag Kohfidisch 2023 (Gemeinde) Voranschlag der Gemeinde - Einnahmen und Ausgaben Wirtschaft und Finanzen Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... 10908 Kohfidisch Creative Commons Attribution License 3.0 ... Gemeinde Kohfidisch: Municipal Spending (closed account... Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... NaN NaN https://www.offenerhaushalt.at NaN https://creativecommons.org/licenses/by/3.0/ NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
2 2 3 03986e54-6367-41aa-bcba-8c51036237dc 2023-07-02 Rechnungsabschluss Kohfidisch 2022 (Gemeinde) Rechnungsabschluss der Gemeinde - Einnahmen un... Wirtschaft und Finanzen Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... 10908 Kohfidisch Creative Commons Attribution License 3.0 ... Gemeinde Kohfidisch: Municipal Spending (closed account... Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... NaN NaN https://www.offenerhaushalt.at NaN https://creativecommons.org/licenses/by/3.0/ NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
3 3 4 3e54878c-8657-4268-ad6e-064c3869169e 2023-07-02 Rechnungsabschluss Kohfidisch 2021 (Statistik ... Rechnungsabschluss der Gemeinde - Einnahmen un... Wirtschaft und Finanzen Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... 10908 Kohfidisch Creative Commons Attribution License 3.0 ... Statistik Austria Kohfidisch: Municipal Spending (closed account... Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... NaN NaN https://www.offenerhaushalt.at NaN https://creativecommons.org/licenses/by/3.0/ NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
4 4 5 0718837e-ffba-4833-b38f-a56b8ce0c012 2023-07-02 Rechnungsabschluss Kohfidisch 2020 (Statistik ... Rechnungsabschluss der Gemeinde - Einnahmen un... Wirtschaft und Finanzen Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... 10908 Kohfidisch Creative Commons Attribution License 3.0 ... Statistik Austria Kohfidisch: Municipal Spending (closed account... Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... NaN NaN https://www.offenerhaushalt.at NaN https://creativecommons.org/licenses/by/3.0/ NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
14132 14144 14145 3fe4b483-0a4f-4dab-94ed-076fe22b770b 2022-01-10 Voranschlag Pöndorf 2021 (Gemeinde) Voranschlag der Gemeinde - Einnahmen und Ausgaben Wirtschaft und Finanzen Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... 41726 Pöndorf Creative Commons Namensnennung 4.0 International ... Gemeinde Pöndorf: Municipal Spending (closed accounts) ... Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... NaN NaN https://www.offenerhaushalt.at NaN https://creativecommons.org/licenses/by/4.0/de... NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
14133 14145 14146 f21ab3d9-ba40-461f-9576-52285a030a31 2022-01-10 Rechnungsabschluss Pöndorf 2020 (Gemeinde) Rechnungsabschluss der Gemeinde - Einnahmen un... Wirtschaft und Finanzen Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... 41726 Pöndorf Creative Commons Namensnennung 4.0 International ... Gemeinde Pöndorf: Municipal Spending (closed accounts) ... Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... NaN NaN https://www.offenerhaushalt.at NaN https://creativecommons.org/licenses/by/4.0/de... NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
14134 14146 14147 36aa0889-39cd-4d4b-b17b-130e53af4c4a 2022-01-10 Rechnungsabschluss Mauthausen 2020 (Gemeinde) Rechnungsabschluss der Gemeinde - Einnahmen un... Wirtschaft und Finanzen Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... 41111 Mauthausen Creative Commons Namensnennung 4.0 International ... Gemeinde Mauthausen: Municipal Spending (closed account... Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... NaN NaN https://www.offenerhaushalt.at NaN https://creativecommons.org/licenses/by/4.0/de... NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
14135 14147 14148 05269861-a62f-4b95-8bc1-43a778984208 2022-01-10 Rechnungsabschluss Purgstall an der Erlauf 202... Rechnungsabschluss der Gemeinde - Einnahmen un... Wirtschaft und Finanzen Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... 32008 Purgstall an der Erlauf Creative Commons Namensnennung 4.0 International ... Gemeinde Purgstall an der Erlauf: Municipal Spending (c... Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... NaN NaN https://www.offenerhaushalt.at NaN https://creativecommons.org/licenses/by/4.0/de... NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
14136 14148 14149 36cbb257-de7d-484a-80e2-209177977117 2022-01-10 Voranschlag Aschach an der Steyr 2021 (Gemeinde) Voranschlag der Gemeinde - Einnahmen und Ausgaben Wirtschaft und Finanzen Ausgaben, Budget, Einnahmen, Ergebnishaushalt,... 41502 Aschach an der Steyr Creative Commons Namensnennung 4.0 International ... Gemeinde Aschach an der Steyr: Municipal Spending (clos... Datenquelle: CC-BY-4.0: KDZ - www.offenerhaush... NaN NaN https://www.offenerhaushalt.at NaN https://creativecommons.org/licenses/by/4.0/de... NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...

14137 rows × 33 columns

In [14]:
# Converting the metrics to a dictionary and creating a seperate df out of it
def convert_metrics_to_dict(metric_list):
    return {d['metric']: d['score'] for d in metric_list}

# Apply the function to the 'metrics' column
metrics_dicts = df['FAIR_assessment'].apply(convert_metrics_to_dict)

# Create a new DataFrame from the list of dictionaries
metrics_df = pd.DataFrame(metrics_dicts.tolist())


metrics_df
Out[14]:
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3
0 2 0 1 1 2 2 1 1 0 2 2 1
1 2 0 1 1 2 2 1 1 0 2 2 1
2 2 0 1 1 2 2 1 1 0 2 2 1
3 2 0 1 1 2 2 1 1 0 2 2 1
4 2 0 1 1 2 2 1 1 0 2 2 1
... ... ... ... ... ... ... ... ... ... ... ... ...
14132 2 0 1 1 2 2 1 1 0 2 2 1
14133 2 0 1 1 2 2 1 1 0 2 2 1
14134 2 0 1 1 2 2 1 1 0 2 2 1
14135 2 0 1 1 2 2 1 1 0 2 2 1
14136 2 0 1 1 2 2 1 1 0 2 2 1

14137 rows × 12 columns

In [16]:
# Combining the metadata information and the table that contains the scores for the metrics
df_combined = pd.concat([df, metrics_df], axis=1, ignore_index=True)
df_combined = df_combined.drop(columns = [0,1])

df_combined.columns
In [41]:
df_combined.loc[:, 33:]
Out[41]:
33 34 35 36 37 38 39 40 41 42 43 44
0 2 0 1 1 2 2 1 1 0 2 2 1
1 2 0 1 1 2 2 1 1 0 2 2 1
2 2 0 1 1 2 2 1 1 0 2 2 1
3 2 0 1 1 2 2 1 1 0 2 2 1
4 2 0 1 1 2 2 1 1 0 2 2 1
... ... ... ... ... ... ... ... ... ... ... ... ...
14132 2 0 1 1 2 2 1 1 0 2 2 1
14133 2 0 1 1 2 2 1 1 0 2 2 1
14134 2 0 1 1 2 2 1 1 0 2 2 1
14135 2 0 1 1 2 2 1 1 0 2 2 1
14136 2 0 1 1 2 2 1 1 0 2 2 1

14137 rows × 12 columns

In [45]:
columns = df_combined.columns
In [47]:
# Lost the column names in the meantime so renaming them so we now which column corresponds to which metric
new_column_names = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1','A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']

# Renaming the last 12 columns - the ones containing the metrics
df_combined.rename(columns=dict(zip(columns[-12:], new_column_names)), inplace=True)

print(df_combined.head())

                                      2           3  \
0  3E7BDC0B-6CBC-5A25-AF07-409E1EA40408  2023-07-03   
1  d7e2c489-c233-4458-9d98-a2048a73a43e  2023-07-02   
2  03986e54-6367-41aa-bcba-8c51036237dc  2023-07-02   
3  3e54878c-8657-4268-ad6e-064c3869169e  2023-07-02   
4  0718837e-ffba-4833-b38f-a56b8ce0c012  2023-07-02   

                                                   4  \
0  Entwicklung nachhaltiger Schilferntetechniken ...   
1             Voranschlag Kohfidisch 2023 (Gemeinde)   
2      Rechnungsabschluss Kohfidisch 2022 (Gemeinde)   
3  Rechnungsabschluss Kohfidisch 2021 (Statistik ...   
4  Rechnungsabschluss Kohfidisch 2020 (Statistik ...   

                                                   5                        6  \
0                                                NaN                   Umwelt   
1  Voranschlag der Gemeinde - Einnahmen und Ausgaben  Wirtschaft und Finanzen   
2  Rechnungsabschluss der Gemeinde - Einnahmen un...  Wirtschaft und Finanzen   
3  Rechnungsabschluss der Gemeinde - Einnahmen un...  Wirtschaft und Finanzen   
4  Rechnungsabschluss der Gemeinde - Einnahmen un...  Wirtschaft und Finanzen   

                                                   7  \
0  Nationalparks Austria, Naturschutz, OpenDocume...   
1  Ausgaben, Budget, Einnahmen, Ergebnishaushalt,...   
2  Ausgaben, Budget, Einnahmen, Ergebnishaushalt,...   
3  Ausgaben, Budget, Einnahmen, Ergebnishaushalt,...   
4  Ausgaben, Budget, Einnahmen, Ergebnishaushalt,...   

                               8                                         9  \
0  NP Neusiedler See - Seewinkel  Creative Commons Attribution License 3.0   
1               10908 Kohfidisch  Creative Commons Attribution License 3.0   
2               10908 Kohfidisch  Creative Commons Attribution License 3.0   
3               10908 Kohfidisch  Creative Commons Attribution License 3.0   
4               10908 Kohfidisch  Creative Commons Attribution License 3.0   

           10                     11  ... F2A F2B A1.1 A1.2 I1 I2 I3 R1.1  \
0  2022-04-01  Nationalparks Austria  ...   1   1    2    2  1  1  0    2   
1  2023-01-01     Offenerhaushalt.at  ...   1   1    2    2  1  1  0    2   
2  2022-01-01     Offenerhaushalt.at  ...   1   1    2    2  1  1  0    2   
3  2021-01-01     Offenerhaushalt.at  ...   1   1    2    2  1  1  0    2   
4  2020-01-01     Offenerhaushalt.at  ...   1   1    2    2  1  1  0    2   

  R1.2 R1.3  
0    2    1  
1    2    1  
2    2    1  
3    2    1  
4    2    1  

[5 rows x 43 columns]
In [53]:
# Converting the metric columns to numerical
for col in new_column_names:
    df_combined[col] = pd.to_numeric(df_combined[col], errors='coerce')
    
# Summing the last 12 columns row-wise and storing the result in a new column called 'Score'
df_combined['Score'] = df_combined[new_column_names].sum(axis=1)
In [59]:
# First look at the results
print_summary_stats(df_combined)

Mean: 14.021220909669662
Median: 15.0
Max: 19
Min: 0
In [62]:
# Removing the limit so all the columns are displayed 
pd.set_option('display.max_columns', None)

Processing the files from the German data portal¶

In [5]:
df2 = pd.read_csv(r"./ogdmetadatendatagvat_assessed_german2024-06-10.csv", index_col = [0])
In [6]:
df2.head()
Out[6]:
author author_email creator_user_id id isopen license_id license_title maintainer maintainer_email metadata_created ... url version extras groups resources tags relationships_as_subject relationships_as_object FAIR_assessment assessment_status_code
0 Hanse- und Universitätsstadt Rostock – Hauptamt geodienste@rostock.de f512f73d-bed2-439d-8f6f-3fbc6048b24e 05be0aa5-76a2-4850-b37f-69e623565fbe False NaN NaN Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de 2023-01-06T06:14:11.920576 ... https://www.opendata-hro.de/dataset/e7c10dd4-d... NaN [{'key': 'access_rights', 'value': 'http://pub... [{'description': '', 'display_name': 'Regierun... [{'access_url': 'https://geo.sv.rostock.de/dow... [{'display_name': 'politik', 'id': 'a8e6e4aa-6... [] [] [{'metric': 'F1A', 'score': '2', 'target_uri':... 200
1 Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de f512f73d-bed2-439d-8f6f-3fbc6048b24e 44fa7d61-7924-4740-95ec-b4a6821139fe False NaN NaN Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de 2024-05-08T06:31:53.940777 ... https://www.opendata-hro.de/dataset/c276228b-9... NaN [{'key': 'access_rights', 'value': 'http://pub... [{'description': '', 'display_name': 'Regierun... [{'access_url': 'https://geo.sv.rostock.de/dow... [{'display_name': 'bauleitplanung', 'id': '861... [] [] [{'metric': 'F1A', 'score': '2', 'target_uri':... 200
2 Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de f512f73d-bed2-439d-8f6f-3fbc6048b24e 6219e4c3-e819-40f0-86cd-543ff829ad6c False NaN NaN Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de 2024-05-08T06:31:46.141017 ... https://www.opendata-hro.de/dataset/655f46c2-1... NaN [{'key': 'access_rights', 'value': 'http://pub... [{'description': '', 'display_name': 'Regierun... [{'access_url': 'https://geo.sv.rostock.de/dow... [{'display_name': 'bauleitplanung', 'id': '861... [] [] [{'metric': 'F1A', 'score': '2', 'target_uri':... 200
3 Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de f512f73d-bed2-439d-8f6f-3fbc6048b24e 7c46a2cc-4fe3-4399-8833-61b016730b71 False NaN NaN Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de 2024-05-08T06:32:00.483952 ... https://www.opendata-hro.de/dataset/7afdfa7a-7... NaN [{'key': 'access_rights', 'value': 'http://pub... [{'description': '', 'display_name': 'Bildung,... [{'access_url': 'https://geo.sv.rostock.de/dow... [{'display_name': 'freizeit', 'id': '73a5f345-... [] [] [{'metric': 'F1A', 'score': '2', 'target_uri':... 200
4 Hanse- und Universitätsstadt Rostock – Hauptamt geodienste@rostock.de f512f73d-bed2-439d-8f6f-3fbc6048b24e 4d25536c-7cc4-4761-8504-0ba8ca320fa1 False NaN NaN Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de 2024-05-08T06:32:18.276436 ... https://www.opendata-hro.de/dataset/aceccfc0-d... NaN [{'key': 'access_rights', 'value': 'http://pub... [{'description': '', 'display_name': 'Regierun... [{'access_url': 'https://geo.sv.rostock.de/dow... [{'display_name': 'behörde', 'id': 'e15183d8-d... [] [] [{'metric': 'F1A', 'score': '2', 'target_uri':... 200

5 rows × 31 columns

In [7]:
df2 = df2[df2["assessment_status_code"] == 200]
df2 = df2.drop(labels=["assessment_status_code"], axis=1).reset_index(drop=True)
df2["FAIR_assessment"] = df2["FAIR_assessment"].map(lambda x: build_assessment_json(x))
df2
Out[7]:
author author_email creator_user_id id isopen license_id license_title maintainer maintainer_email metadata_created ... type url version extras groups resources tags relationships_as_subject relationships_as_object FAIR_assessment
0 Hanse- und Universitätsstadt Rostock – Hauptamt geodienste@rostock.de f512f73d-bed2-439d-8f6f-3fbc6048b24e 05be0aa5-76a2-4850-b37f-69e623565fbe False NaN NaN Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de 2023-01-06T06:14:11.920576 ... dataset https://www.opendata-hro.de/dataset/e7c10dd4-d... NaN [{'key': 'access_rights', 'value': 'http://pub... [{'description': '', 'display_name': 'Regierun... [{'access_url': 'https://geo.sv.rostock.de/dow... [{'display_name': 'politik', 'id': 'a8e6e4aa-6... [] [] [{'metric': 'F1A', 'score': '2'}, {'metric': '...
1 Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de f512f73d-bed2-439d-8f6f-3fbc6048b24e 44fa7d61-7924-4740-95ec-b4a6821139fe False NaN NaN Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de 2024-05-08T06:31:53.940777 ... dataset https://www.opendata-hro.de/dataset/c276228b-9... NaN [{'key': 'access_rights', 'value': 'http://pub... [{'description': '', 'display_name': 'Regierun... [{'access_url': 'https://geo.sv.rostock.de/dow... [{'display_name': 'bauleitplanung', 'id': '861... [] [] [{'metric': 'F1A', 'score': '2'}, {'metric': '...
2 Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de f512f73d-bed2-439d-8f6f-3fbc6048b24e 6219e4c3-e819-40f0-86cd-543ff829ad6c False NaN NaN Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de 2024-05-08T06:31:46.141017 ... dataset https://www.opendata-hro.de/dataset/655f46c2-1... NaN [{'key': 'access_rights', 'value': 'http://pub... [{'description': '', 'display_name': 'Regierun... [{'access_url': 'https://geo.sv.rostock.de/dow... [{'display_name': 'bauleitplanung', 'id': '861... [] [] [{'metric': 'F1A', 'score': '2'}, {'metric': '...
3 Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de f512f73d-bed2-439d-8f6f-3fbc6048b24e 7c46a2cc-4fe3-4399-8833-61b016730b71 False NaN NaN Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de 2024-05-08T06:32:00.483952 ... dataset https://www.opendata-hro.de/dataset/7afdfa7a-7... NaN [{'key': 'access_rights', 'value': 'http://pub... [{'description': '', 'display_name': 'Bildung,... [{'access_url': 'https://geo.sv.rostock.de/dow... [{'display_name': 'freizeit', 'id': '73a5f345-... [] [] [{'metric': 'F1A', 'score': '2'}, {'metric': '...
4 Hanse- und Universitätsstadt Rostock – Hauptamt geodienste@rostock.de f512f73d-bed2-439d-8f6f-3fbc6048b24e 4d25536c-7cc4-4761-8504-0ba8ca320fa1 False NaN NaN Hanse- und Universitätsstadt Rostock – Kataste... geodienste@rostock.de 2024-05-08T06:32:18.276436 ... dataset https://www.opendata-hro.de/dataset/aceccfc0-d... NaN [{'key': 'access_rights', 'value': 'http://pub... [{'description': '', 'display_name': 'Regierun... [{'access_url': 'https://geo.sv.rostock.de/dow... [{'display_name': 'behörde', 'id': 'e15183d8-d... [] [] [{'metric': 'F1A', 'score': '2'}, {'metric': '...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
9069 NaN NaN f512f73d-bed2-439d-8f6f-3fbc6048b24e bed1ad07-1963-4175-bb73-0af79479eede False NaN NaN NaN NaN 2021-06-02T11:24:14.680308 ... dataset https://www.berlin.de/lageso/gesundheit/infekt... NaN [{'key': 'conforms_to', 'value': '["http://dca... [{'description': '', 'display_name': 'Gesundhe... [{'access_url': 'https://www.berlin.de/lageso/... [{'display_name': 'covid-19_erkrankungen', 'id... [] [] [{'metric': 'F1A', 'score': '2'}, {'metric': '...
9070 NaN NaN f512f73d-bed2-439d-8f6f-3fbc6048b24e 80c44751-ed80-4433-99f5-4baf108e1155 False NaN NaN NaN NaN 2021-06-02T11:21:15.752075 ... dataset https://www.berlin.de/lageso/gesundheit/infekt... NaN [{'key': 'conforms_to', 'value': '["http://dca... [{'description': '', 'display_name': 'Gesundhe... [{'access_url': 'https://www.berlin.de/lageso/... [{'display_name': 'covid-19_erkrankungen', 'id... [] [] [{'metric': 'F1A', 'score': '2'}, {'metric': '...
9071 NaN NaN f512f73d-bed2-439d-8f6f-3fbc6048b24e daaf2c24-652d-474b-a9ae-aad1ec9a3a6c False NaN NaN NaN NaN 2024-01-05T08:52:48.735472 ... dataset https://www.berlin.de/sen/arbeit/weiterbildung... NaN [{'key': 'conforms_to', 'value': '["http://dca... [{'description': '', 'display_name': 'Wirtscha... [{'access_url': 'https://www.berlin.de/sen/arb... [{'display_name': 'bildungszeit', 'id': '3e3a2... [] [] [{'metric': 'F1A', 'score': '2'}, {'metric': '...
9072 NaN NaN f512f73d-bed2-439d-8f6f-3fbc6048b24e 092f7b48-66bd-465d-ab94-49c237470fdf False NaN NaN NaN NaN 2018-07-27T08:08:07.793153 ... dataset https://www.berlin.de/lageso/gesundheit/gesund... NaN [{'key': 'conforms_to', 'value': '["http://dca... [{'description': '', 'display_name': 'Gesundhe... [{'access_url': 'https://www.berlin.de/lageso/... [{'display_name': 'badegewässer', 'id': '4bc11... [] [] [{'metric': 'F1A', 'score': '2'}, {'metric': '...
9073 NaN NaN f512f73d-bed2-439d-8f6f-3fbc6048b24e 2444f126-3d4a-4421-8d40-a8ddc4b73a7a False NaN NaN NaN NaN 2024-02-02T07:46:07.127646 ... dataset https://www.berlin.de/osz/osz-karte/ NaN [{'key': 'conforms_to', 'value': '["http://dca... [{'description': '', 'display_name': 'Bildung,... [{'access_url': 'https://www.berlin.de/osz/osz... [{'display_name': 'berufsschule', 'id': '9f91c... [] [] [{'metric': 'F1A', 'score': '2'}, {'metric': '...

9074 rows × 30 columns

In [10]:
metrics_dicts = df2['FAIR_assessment'].apply(convert_metrics_to_dict)

# Create a new DataFrame from the list of dictionaries
metrics_df2 = pd.DataFrame(metrics_dicts.tolist())

df_combined2 = pd.concat([df2, metrics_df2], axis=1, ignore_index=True)


columns = df_combined2.columns

new_column_names = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1','A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']

# Renaming the last 12 columns
df_combined2.rename(columns=dict(zip(columns[-12:], new_column_names)), inplace=True)

for col in new_column_names:
    df_combined2[col] = pd.to_numeric(df_combined2[col], errors='coerce')
    
    
df_combined2['Score'] = df_combined2[new_column_names].sum(axis=1)

print_summary_stats(df_combined2)

Mean: 6.320255675556536
Median: 5.0
Max: 16
Min: 0

Files from the Swiss data portal¶

In [12]:
df3 = pd.read_csv(r"./ogdmetadatendatagvat_assessed_swiss2024-06-12.csv", index_col = [0])
In [14]:
df3 = df3[df3["assessment_status_code"] == 200]
df3 = df3.drop(labels=["assessment_status_code"], axis=1).reset_index(drop=True)
df3["FAIR_assessment"] = df3["FAIR_assessment"].map(lambda x: build_assessment_json(x))
df3
Out[14]:
Unnamed: 0 owner_org maintainer issued title_for_slug qualified_relations private maintainer_email num_tags contact_points ... documentation modified url see_alsos revision_id identifier spatial coverage extras FAIR_assessment
0 0 b3d5c5d8-f736-459e-b23b-fe0ea6196cf2 Sara Schärrer 2023-03-02T00:00:00 Tierseuchenmeldungen in der Schweiz [] False sara.schaerrer@blv.admin.ch 9 [{'name': 'Kompetenzzentrum Daten', 'email': '... ... [] NaN https://infosm.blv.admin.ch/ [] c8793a79-b0ff-4e59-98bc-0dec70e90e88 tierseuchen@bundesamt-fur-lebensmittelsicherhe... NaN NaN NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
1 25 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2023-08-30T00:00:00 klimaanalyse-windgeschwindigkeit-nacht-zukunft... [] False NaN 0 [{'email': 'afu@bd.so.ch', 'name': 'afu@bd.so.... ... NaN 2023-08-30T00:00:00 https://data.geo.so.ch?filter=ch.so.afu.klimaa... [] 5d7ec063-fd29-4322-9c33-04bc607e72c1 f5312f7d-dba5-4ca3-8419-de3fea19d6c7@kanton_so... Kanton Solothurn (SO) NaN [{'key': 'harvest_object_id', 'value': '230dfa... [{'metric': 'F1A', 'score': '2'}, {'metric': '...
2 26 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2024-06-06T00:00:00 gebaudeflachen-mit-adressen-amtliche-vermessung2 [] False NaN 0 [{'email': 'agi@bd.so.ch', 'name': 'agi@bd.so.... ... NaN 2024-06-06T00:00:00 https://data.geo.so.ch?filter=ch.so.agi.av.geb... [] cd1c1574-bab5-46a7-a55b-13d026cc857c 1e984455-1980-4ba0-b87c-633b0971d99d@kanton_so... Kanton Solothurn (SO) NaN [{'key': 'harvest_object_id', 'value': '10425c... [{'metric': 'F1A', 'score': '2'}, {'metric': '...
3 27 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2024-01-31T00:00:00 kantonsstrassen1 [] False NaN 0 [{'email': 'avt@bd.so.ch', 'name': 'avt@bd.so.... ... ['https://geo.so.ch/map?l=ch.so.avt.kantonsstr... 2024-01-31T00:00:00 https://data.geo.so.ch?filter=ch.so.avt.kanton... [] 4491b576-5897-4ea9-9f89-fcc121cec591 9209c1e4-6adf-452b-9870-235a6dbe01bb@kanton_so... Kanton Solothurn (SO) NaN [{'key': 'harvest_object_id', 'value': 'd4e9a6... [{'metric': 'F1A', 'score': '2'}, {'metric': '...
4 29 51ffc9d5-bf18-4bb4-9b26-ad9b45a60e14 Stadt Winterthur, Stadtentwicklung der Stadt W... 2022-09-20T17:10:34+01:00 beschaftigtendichte-in-der-stadt-winterthur-ab... [] False statistik@win.ch 4 [{'email': 'statistik@win.ch', 'name': 'Stadt ... ... [] 2024-06-04T16:54:18+01:00 https://stadt.winterthur.ch/themen/die-stadt/w... [{'dataset_identifier': '1521@stadt-winterthur... 449f19b9-7b35-4d6e-beeb-2f3492da35e6 1621@stadt-winterthur NaN NaN [{'value': '1621@stadt-winterthur', 'key': 'gu... [{'metric': 'F1A', 'score': '2'}, {'metric': '...
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
10253 11886 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2023-02-08T00:00:00 gebaude-digitales-oberflachenmodell-lidar-2018 NaN False NaN 0 [{'email': 'agi@bd.so.ch', 'name': 'agi@bd.so.... ... NaN 2023-02-08T00:00:00 https://data.geo.so.ch?filter=ch.bl.agi.lidar_... {} 30c3a281-a904-47d6-ae6c-fc0429ff41b3 bfdbe5cc-f512-4ef3-bafa-8aefbe04c352@kanton_so... Kanton Solothurn (SO) NaN NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
10254 11887 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2023-01-18T00:00:00 administrative-einteilung-av-und-grundbuch NaN False NaN 0 [{'email': 'agi@bd.so.ch', 'name': 'agi@bd.so.... ... NaN 2023-01-18T00:00:00 https://data.geo.so.ch?filter=ch.so.agi.av.adm... {} 1cf92303-d3d8-4cd9-94a5-8532dce645c3 d596090e-2f5f-4c42-885c-477b72dc2ed7@kanton_so... Kanton Solothurn (SO) NaN NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
10255 11888 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2023-03-01T00:00:00 strassenlarm NaN False NaN 0 [{'email': 'avt@bd.so.ch', 'name': 'avt@bd.so.... ... NaN 2023-03-01T00:00:00 https://data.geo.so.ch?filter=ch.so.avt.strass... {} e5feee8d-48cc-4a29-bf79-fd60eb0a9c16 debd0f63-9127-4585-bca6-1f6832872472@kanton_so... Kanton Solothurn (SO) NaN NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
10256 11889 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2023-02-08T00:00:00 orthofoto-2012-rgb NaN False NaN 0 [{'email': 'agi@bd.so.ch', 'name': 'agi@bd.so.... ... NaN 2023-02-08T00:00:00 https://data.geo.so.ch?filter=ch.so.agi.orthof... {} ab641c3d-478e-4cfe-8ad0-eb69f616e6a2 cb8d5956-0b22-4ec0-be97-ffbdc2fcbd1d@kanton_so... Kanton Solothurn (SO) NaN NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...
10257 11890 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2023-03-01T00:00:00 waldwanderung NaN False NaN 0 [{'email': 'awjf@vd.so.ch', 'name': 'awjf@vd.s... ... NaN 2023-03-01T00:00:00 https://data.geo.so.ch?filter=ch.so.awjf.waldw... {} 8a532052-8def-47d9-9439-cec72d386c24 5e9abe1f-eeaa-4771-8830-82f192e365bd@kanton_so... Kanton Solothurn (SO) NaN NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '...

10258 rows × 51 columns

In [15]:
metrics_dicts = df3['FAIR_assessment'].apply(convert_metrics_to_dict)

# Create a new DataFrame from the list of dictionaries
metrics_df3 = pd.DataFrame(metrics_dicts.tolist())

df_combined3 = pd.concat([df3, metrics_df3], axis=1, ignore_index=True)


columns = df_combined3.columns

new_column_names = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1','A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']

# Renaming the last 12 columns
df_combined3.rename(columns=dict(zip(columns[-12:], new_column_names)), inplace=True)

for col in new_column_names:
    df_combined3[col] = pd.to_numeric(df_combined3[col], errors='coerce')
    
    
df_combined3['Score'] = df_combined3[new_column_names].sum(axis=1)

Mean: 6.320255675556536
Median: 5.0
Max: 16
Min: 0

Files from the Irish data portal¶

In [27]:
df4 = pd.read_csv(r"./ogdmetadatendatagvat_assessed_irish2024-06-12.csv", index_col = [0])
In [28]:
len(df4)
Out[28]:
15000
In [29]:
df4 = df4[df4["assessment_status_code"] == 200]
df4 = df4.drop(labels=["assessment_status_code"], axis=1).reset_index(drop=True)
In [32]:
# When running the same functions we encountered an issue with JSON formatting
# So we slightly modified the code such that it handles the error
def build_assessment_json2(assessment):
    regex_result = re.findall(r"'metric': '.*?', 'score': '.*?'", assessment)
    assessment_list = []
    for result in regex_result:
        result = result.replace("\'", "\"")  # Replace single quotes with double quotes
        json_string = "{" + result + "}"  # Construct the JSON string
        try:
            print(f"Parsing JSON string: {json_string}")  # Debug print statement
            assessment_dict = json.loads(json_string)  # Parse JSON string
            assessment_list.append(assessment_dict)  # Append to list
        except json.JSONDecodeError as e:
            print(f"Error parsing JSON string: {json_string}")
            print(f"JSONDecodeError: {e}")
    return assessment_list
In [33]:
df4["FAIR_assessment"] = df4["FAIR_assessment"].map(lambda x: build_assessment_json2(x))

Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "2"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "2"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "2"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "2"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "2"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "2"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "1"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "1"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "1"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "1"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "1"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "1"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "1"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "1"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "1"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "2"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "2"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "2"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "1"}
Parsing JSON string: {"metric": "F2B", "score": "1"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "1"}
Parsing JSON string: {"metric": "I2", "score": "1"}
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Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
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Parsing JSON string: {"metric": "F1A", "score": "2"}
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Parsing JSON string: {"metric": "F2A", "score": "0"}
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Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
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Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
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Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}
Parsing JSON string: {"metric": "F1A", "score": "2"}
Parsing JSON string: {"metric": "F1B", "score": "0"}
Parsing JSON string: {"metric": "F2A", "score": "0"}
Parsing JSON string: {"metric": "F2B", "score": "0"}
Parsing JSON string: {"metric": "A1.1", "score": "2"}
Parsing JSON string: {"metric": "A1.2", "score": "0"}
Parsing JSON string: {"metric": "I1", "score": "0"}
Parsing JSON string: {"metric": "I2", "score": "0"}
Parsing JSON string: {"metric": "I3", "score": "0"}
Parsing JSON string: {"metric": "R1.1", "score": "0"}
Parsing JSON string: {"metric": "R1.2", "score": "0"}
Parsing JSON string: {"metric": "R1.3", "score": "0"}

IOPub data rate exceeded.
The notebook server will temporarily stop sending output
to the client in order to avoid crashing it.
To change this limit, set the config variable
`--NotebookApp.iopub_data_rate_limit`.

Current values:
NotebookApp.iopub_data_rate_limit=1000000.0 (bytes/sec)
NotebookApp.rate_limit_window=3.0 (secs)
In [34]:
metrics_dicts = df4['FAIR_assessment'].apply(convert_metrics_to_dict)

# Create a new DataFrame from the list of dictionaries
metrics_df4 = pd.DataFrame(metrics_dicts.tolist())

df_combined4 = pd.concat([df4, metrics_df4], axis=1, ignore_index=True)


columns = df_combined4.columns

new_column_names = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1','A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']

# Renaming the last 12 columns
df_combined4.rename(columns=dict(zip(columns[-12:], new_column_names)), inplace=True)

for col in new_column_names:
    df_combined4[col] = pd.to_numeric(df_combined4[col], errors='coerce')
    
    
df_combined4['Score'] = df_combined4[new_column_names].sum(axis=1)

Re-running the process on Austrian files with the exact same steps so the format of the results are identical¶

In [24]:
df = df[df["assessment_status_code"] == 200]
df = df.drop(labels=["assessment_status_code"], axis=1).reset_index(drop=True)
df["FAIR_assessment"] = df["FAIR_assessment"].map(lambda x: build_assessment_json(x))
In [25]:
metrics_dicts = df['FAIR_assessment'].apply(convert_metrics_to_dict)

# Create a new DataFrame from the list of dictionaries
metrics_df = pd.DataFrame(metrics_dicts.tolist())

df_combined = pd.concat([df, metrics_df], axis=1, ignore_index=True)


columns = df_combined.columns

new_column_names = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1','A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']

# Renaming the last 12 columns
df_combined.rename(columns=dict(zip(columns[-12:], new_column_names)), inplace=True)

for col in new_column_names:
    df_combined[col] = pd.to_numeric(df_combined[col], errors='coerce')
    
    
df_combined['Score'] = df_combined[new_column_names].sum(axis=1)
In [35]:
print_summary_stats(df_combined)
print_summary_stats(df_combined2)
print_summary_stats(df_combined3)
print_summary_stats(df_combined4)

Mean: 14.021220909669662
Median: 15.0
Max: 19
Min: 0
Mean: 6.320255675556536
Median: 5.0
Max: 16
Min: 0
Mean: 9.01550009748489
Median: 9.0
Max: 24
Min: 0
Mean: 5.2843391902215435
Median: 4.0
Max: 22.0
Min: 0.0
In [84]:
import os
os.getcwd()
Out[84]:
'C:\\Users\\MolnarM\\Desktop\\Notebooks\\Uni'
In [85]:
df.to_excel('austria_df.xlsx')
df2.to_excel('germany_df.xlsx')
df3.to_excel('switzerland_df.xlsx')
df4.to_excel('ireland_df.xlsx')
In [76]:
df_combined4.columns
Out[76]:
Index([        0,         1,         2,         3,         4,         5,
               6,         7,         8,         9,        10,        11,
              12,        13,        14,        15,        16,        17,
              18,        19,        20,        21,        22,        23,
              24,        25,        26,        27,        28,        29,
              30,        31,        32,        33,        34,        35,
              36,        37,        38,        39,        40,        41,
              42,        43,        44,        45,        46,        47,
              48,        49,        50,        51,        52,        53,
              54,        55,        56,     'F1A',     'F1B',     'F2A',
           'F2B',    'A1.1',    'A1.2',      'I1',      'I2',      'I3',
          'R1.1',    'R1.2',    'R1.3',   'Score', 'Country'],
      dtype='object')
In [74]:
df_combined4
Out[74]:
0 1 2 3 4 5 6 7 8 9 ... A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3 Score Country
0 NaN NaN NaN NaN NaN NaN 0863c6bf-d7d8-485b-94b7-d50a11d5796d Continuous f2d3c4fc-0ea7-4959-98c5-598241a5ea51 True ... 2.0 0 0 0 0 0 0 0 4.0 Ireland
1 NaN NaN NaN opendata@tailte.ie NaN NaN f98da442-3307-463d-b676-a327e93755ec Continuous 6eccd685-3dec-481b-9b9f-3f957ba9ddcd True ... 2.0 0 1 2 0 0 0 2 12.0 Ireland
2 NaN NaN NaN opendata@met.ie Kieran Harper NaN f1eafd65-adc1-4721-aefc-fa9b6c9b3a0c Continuous 0a8b1aa8-f49b-411e-a120-b0cd0297c2a1 True ... 2.0 0 1 2 0 0 0 2 12.0 Ireland
3 NaN NaN swagger 2.0, Akoma Ntoso open.data@oireachtas.ie Open Data Liaison Officer (01) 6183000 ef08264c-676d-48c5-aaad-6ce8aa18d521 Continuously updated d9ddc7ee-d497-41bb-924f-3557cfa12037 True ... 2.0 0 0 0 0 0 0 0 4.0 Ireland
4 NaN NaN NaN NaN NaN NaN ef08264c-676d-48c5-aaad-6ce8aa18d521 Weekly 18020dbe-8565-47d0-8632-a8fc4dc540a0 True ... 2.0 0 0 0 0 0 0 0 4.0 Ireland
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
13085 NaN NaN NaN NaN NaN NaN 4bb8ac75-49de-49b1-8568-ea7ba09886f8 NaN bc4dc2d9-1c0b-421a-98e0-b4b555402168 True ... 2.0 0 1 2 0 0 0 2 12.0 Ireland
13086 NaN NaN NaN NaN NaN NaN 4bb8ac75-49de-49b1-8568-ea7ba09886f8 NaN 7a1b5db7-ae22-4169-8aa8-19814f32514b True ... 2.0 0 1 2 0 0 0 2 12.0 Ireland
13087 Environmental Protection Agency AIRQD@epa.ie The INSPIRE Directive or INSPIRE lays down a g... NaN NaN NaN ef08264c-676d-48c5-aaad-6ce8aa18d521 Other 2089f142-1f00-4526-9728-ea81fcecc8f9 True ... 2.0 0 0 0 0 0 0 0 4.0 Ireland
13088 Environmental Protection Agency AIRQD@epa.ie The INSPIRE Directive or INSPIRE lays down a g... NaN NaN NaN ef08264c-676d-48c5-aaad-6ce8aa18d521 Other 2803f489-ce32-4543-98ff-d14084b5f1bb True ... 2.0 0 0 0 0 0 0 0 4.0 Ireland
13089 Environmental Protection Agency AIRQD@epa.ie The INSPIRE Directive or INSPIRE lays down a g... NaN NaN NaN ef08264c-676d-48c5-aaad-6ce8aa18d521 Other f7cf61c6-c708-4529-bcce-dc331a630c1c True ... 2.0 0 0 0 0 0 0 0 4.0 Ireland

13090 rows × 71 columns

In [38]:
# Adding a country column so that it can be used for filtering/grouping
df_combined['Country'] = 'Austria'
#df_combined.head()
df_combined2['Country'] = 'Germany'
df_combined3['Country'] = 'Switzerland'
df_combined4['Country'] = 'Ireland'
In [46]:
# The metadata columns are slightly different for each country, only the metrics are identical
# Creating a dataframe that only contains the scores for the metrics (and score and country)
subset_df1 = df_combined.iloc[:, -14:]
subset_df2 = df_combined2.iloc[:, -14:]
subset_df3 = df_combined3.iloc[:, -14:]
subset_df4 = df_combined4.iloc[:, -14:]

# Concatenate the subsets into one big DataFrame
combined_df = pd.concat([subset_df1, subset_df2, subset_df3, subset_df4], axis=0)

# Reset the index to ensure it is unique
combined_df.reset_index(drop=True, inplace=True)
In [47]:
combined_df
Out[47]:
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3 Score Country
0 2 0 1 1.0 2.0 2 1 1 0 2 2 1 15.0 Austria
1 2 0 1 1.0 2.0 2 1 1 0 2 2 1 15.0 Austria
2 2 0 1 1.0 2.0 2 1 1 0 2 2 1 15.0 Austria
3 2 0 1 1.0 2.0 2 1 1 0 2 2 1 15.0 Austria
4 2 0 1 1.0 2.0 2 1 1 0 2 2 1 15.0 Austria
... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
46554 2 0 1 2.0 2.0 0 1 2 0 0 0 2 12.0 Ireland
46555 2 0 1 2.0 2.0 0 1 2 0 0 0 2 12.0 Ireland
46556 2 0 0 0.0 2.0 0 0 0 0 0 0 0 4.0 Ireland
46557 2 0 0 0.0 2.0 0 0 0 0 0 0 0 4.0 Ireland
46558 2 0 0 0.0 2.0 0 0 0 0 0 0 0 4.0 Ireland

46559 rows × 14 columns

In [50]:
# Simple horizontal barplot for average Scores per country
eval1_df = combined_df.groupby("Country").agg({"Score": "mean"})
eval1_df["counts"] = combined_df["Country"].value_counts()
eval1_df = eval1_df.sort_values("counts").reset_index()

plot = plt.figure()
ax = plot.add_axes([0,0,1,1])
fields = eval1_df["Country"]
avg_scores = eval1_df["Score"]

ax.barh(fields, avg_scores, color=counts_color)

plt.show()

F is not FAIR, its Findable!!!!!¶

In [56]:
# Creating subgroups of the metrics corresponding to the FAIR Principles
combined_df['Fair'] = combined_df.iloc[:, 0:4].sum(axis=1)
combined_df['Accessible'] = combined_df.iloc[:, 4:6].sum(axis=1)
combined_df['Interoperable'] = combined_df.iloc[:, 6:9].sum(axis=1)
combined_df['Reusable'] = combined_df.iloc[:, 9:12].sum(axis=1)
In [57]:
combined_df.head()
Out[57]:
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3 Score Country Fair Accessible Interoperable Reusable
0 2 0 1 1.0 2.0 2 1 1 0 2 2 1 15.0 Austria 4.0 4.0 2 5
1 2 0 1 1.0 2.0 2 1 1 0 2 2 1 15.0 Austria 4.0 4.0 2 5
2 2 0 1 1.0 2.0 2 1 1 0 2 2 1 15.0 Austria 4.0 4.0 2 5
3 2 0 1 1.0 2.0 2 1 1 0 2 2 1 15.0 Austria 4.0 4.0 2 5
4 2 0 1 1.0 2.0 2 1 1 0 2 2 1 15.0 Austria 4.0 4.0 2 5
In [59]:
avg_1_4 = combined_df['Fair'].mean()
avg_5_6 = combined_df['Accessible'].mean()
avg_7_9 = combined_df['Interoperable'].mean()
avg_10_12 = combined_df['Reusable'].mean()

# List of perfect scores
perfect_scores = [8, 4, 6, 6]
# List of actual average scores
actual_scores = [avg_1_4, avg_5_6, avg_7_9, avg_10_12]

# Column names
columns = ['Fair', 'Accessible', 'Interoperable', 'Reusable']

# Indices for the bars
indices = np.arange(len(perfect_scores))
width = 0.4  # width of the bars

# Plotting
plt.figure(figsize=(10, 6))
plt.bar(indices, perfect_scores, width=width, color='b', alpha=0.5, label='Perfect Score')
plt.bar(indices, actual_scores, width=0.5*width, color='orange', label='Average Score')

# Customize the plot
plt.xticks(indices, columns)
plt.ylabel('Scores')
plt.title('Average Scores vs Perfect Scores')
plt.legend()

# Show the plot
plt.show()
In [70]:
country_colors = {
    'Austria': 'orange',
    'Germany': 'green',
    'Switzerland': 'red',
    'Ireland': 'purple'
}
aspects = ['Findable', 'Interoperable', 'Accessible', 'Reusable']

# Plotting
fig, ax = plt.subplots(figsize=(12, 8))
width = 0.15  # Width of the individual country bars
perfect_width = width * len(country_colors)  # Width of the perfect score bars
indices = np.arange(len(max_values))

# Plot bars for perfect scores
ax.bar(indices, max_values, width=perfect_width, color='blue', alpha=0.3, label='Perfect Score')

# Plot bars for each country
for i, (country, color) in enumerate(country_colors.items()):
    actual_scores = grouped.loc[country]
    positions = indices + (i - len(country_colors)/2 + 0.5) * width  # Center the smaller bars on the perfect score bar
    ax.bar(positions, actual_scores, width=0.5*width, color=color, label=country)

# Customize the plot
ax.set_xticks(indices)
ax.set_xticklabels(aspects)
ax.set_ylabel('Scores')
ax.set_title('Average Scores vs Perfect Scores by Country and Aspect')
ax.legend()


plt.show()

Summary statistics¶

In [95]:
display(combined_df.groupby('Country').mean())
display(combined_df.groupby('Country').median())
display(combined_df.groupby('Country').min())
display(combined_df.groupby('Country').max())
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3 Score Fair Accessible Interoperable Reusable
Country
Austria 1.989531 0.007003 0.994836 0.994766 1.989531 1.529603 0.994836 0.994766 0.012450 1.529603 1.989531 0.994766 14.021221 3.986136 3.519134 2.002051 4.513900
Germany 1.330395 0.000000 0.690544 0.720079 1.330395 0.059070 0.690544 0.720079 0.000000 0.000000 0.059070 0.720079 6.320256 2.741018 1.389464 1.410624 0.779149
Ireland 1.978915 0.005424 0.192208 0.295606 1.990221 0.009473 0.191444 0.296104 0.007181 0.009778 0.011001 0.297250 5.284339 2.472040 1.999542 0.494729 0.318029
Switzerland 1.978748 0.166212 0.662312 0.823357 1.986157 0.280562 0.662312 0.823357 0.329889 0.280367 0.198674 0.823552 9.015500 3.630630 2.266719 1.815559 1.302593
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3 Score Fair Accessible Interoperable Reusable
Country
Austria 2.0 0.0 1.0 1.0 2.0 2.0 1.0 1.0 0.0 2.0 2.0 1.0 15.0 4.0 4.0 2.0 5.0
Germany 2.0 0.0 1.0 1.0 2.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 5.0 2.0 2.0 2.0 1.0
Ireland 2.0 0.0 0.0 0.0 2.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 4.0 2.0 2.0 0.0 0.0
Switzerland 2.0 0.0 1.0 1.0 2.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 9.0 4.0 2.0 2.0 1.0
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3 Score Fair Accessible Interoperable Reusable
Country
Austria 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0.0 0.0 0.0 0 0
Germany 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0.0 0.0 0.0 0 0
Ireland 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0.0 0.0 0.0 0 0
Switzerland 0 0 0 0.0 0.0 0 0 0 0 0 0 0 0.0 0.0 0.0 0 0
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3 Score Fair Accessible Interoperable Reusable
Country
Austria 2 2 2 1.0 2.0 2 2 1 2 2 2 1 19.0 6.0 4.0 5 5
Germany 2 0 1 2.0 2.0 2 1 2 0 0 2 2 16.0 5.0 4.0 3 4
Ireland 2 2 1 2.0 2.0 2 1 2 2 2 2 2 22.0 7.0 4.0 5 6
Switzerland 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 8.0 4.0 6 6
In [239]:
len(combined_df[combined_df['Score']==24])
Out[239]:
280
In [243]:
# All perfect scores are Swiss datasets - Bioinformatcs theme!
combined_df[(combined_df['Score']==24)&(combined_df['Country']!='Switzerland')]
Out[243]:
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3 Score Country Fair Accessible Interoperable Reusable
In [244]:
combined_df[combined_df['Score']==24]
Out[244]:
F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3 Score Country Fair Accessible Interoperable Reusable
23779 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 Switzerland 8.0 4.0 6 6
23780 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 Switzerland 8.0 4.0 6 6
23782 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 Switzerland 8.0 4.0 6 6
23783 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 Switzerland 8.0 4.0 6 6
23784 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 Switzerland 8.0 4.0 6 6
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
24288 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 Switzerland 8.0 4.0 6 6
24312 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 Switzerland 8.0 4.0 6 6
24330 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 Switzerland 8.0 4.0 6 6
24338 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 Switzerland 8.0 4.0 6 6
24340 2 2 2 2.0 2.0 2 2 2 2 2 2 2 24.0 Switzerland 8.0 4.0 6 6

280 rows × 18 columns

Some visualizations¶

In [96]:
dfq = combined_df.groupby('Country').mean()
In [97]:
import matplotlib.pyplot as plt
import seaborn as sns

# Set the plotting style
sns.set(style="whitegrid")

# Create a bar plot for the average FAIR scores
dfq.plot(kind='bar', figsize=(14, 8))
plt.title('Average FAIR Scores by Country')
plt.xlabel('FAIR Metrics')
plt.ylabel('Average Score')
plt.legend(title='Country')
plt.xticks(rotation=45)
plt.show()

All metric averages correspond to the overall order - no anomalies or significant outliers.

melted Boxplot¶

In [98]:
melted_scores = pd.melt(combined_df, id_vars='Country', var_name='FAIR Metric', value_name='Score')

C:\Users\local_MolnarM\Temp\ipykernel_93292\848975422.py:1: FutureWarning: This dataframe has a column name that matches the 'value_name' column name of the resulting Dataframe. In the future this will raise an error, please set the 'value_name' parameter of DataFrame.melt to a unique name.
  melted_scores = pd.melt(combined_df, id_vars='Country', var_name='FAIR Metric', value_name='Score')
In [99]:
plt.figure(figsize=(14, 8))
sns.boxplot(x='FAIR Metric', y='Score', hue='Country', data=melted_scores)

plt.title('Distribution of FAIR Scores by Country')
plt.xlabel('FAIR Metrics')
plt.ylabel('Score')
plt.xticks(rotation=45)
plt.legend(title='Country')
plt.show()

Austrian and Irish datasets achieve very similar scores (apparently the majority of the datasets are published by a handful of organizations that use consistently the same standards when publishing) - little variation - that's why the boxplots look like this.

Trend analysis¶

(for Austria we use the column metadata_modified for both cases as it does not have a metadata created column)¶

But it yields quite meaningless results as all the metadata was modified in 2022/23 so we leave it out from the visualizations.

In [126]:
# Convert the date columns to datetime
df['metadata_created'] = pd.to_datetime(df['metadata_modified'])
df['metadata_modified'] = pd.to_datetime(df['metadata_modified'])

df2['metadata_created'] = pd.to_datetime(df2['metadata_created'])
df2['metadata_modified'] = pd.to_datetime(df2['metadata_modified'])

df3['metadata_created'] = pd.to_datetime(df3['metadata_created'])
df3['metadata_modified'] = pd.to_datetime(df3['metadata_modified'])

df4['metadata_created'] = pd.to_datetime(df4['metadata_created'])
df4['metadata_modified'] = pd.to_datetime(df4['metadata_modified'])

# Extract the year from the datetime columns
df['year_created'] = df['metadata_created'].dt.year
df['year_modified'] = df['metadata_modified'].dt.year

df2['year_created'] = df2['metadata_created'].dt.year
df2['year_modified'] = df2['metadata_modified'].dt.year

df3['year_created'] = df3['metadata_created'].dt.year
df3['year_modified'] = df3['metadata_modified'].dt.year

df4['year_created'] = df4['metadata_created'].dt.year
df4['year_modified'] = df4['metadata_modified'].dt.year
In [121]:
# Adding back the scores to the original dataframes so we have the column names

df = pd.concat([df,df_combined.iloc[:,-14:]], axis = 1)
df2 = pd.concat([df2,df_combined2.iloc[:,-14:]], axis = 1)
df3 = pd.concat([df3,df_combined3.iloc[:,-14:]], axis = 1)
df4 = pd.concat([df4,df_combined4.iloc[:,-14:]], axis = 1)
In [127]:
# Define the FAIR metrics columns
fair_metrics = ['F1A', 'F1B', 'F2A', 'F2B', 'A1.1', 'A1.2', 'I1', 'I2', 'I3', 'R1.1', 'R1.2', 'R1.3']

# Aggregate average FAIR scores by year for each country
austria_trend = df.groupby('year_created')[fair_metrics].mean().reset_index()
germany_trend = df2.groupby('year_created')[fair_metrics].mean().reset_index()
switzerland_trend = df3.groupby('year_created')[fair_metrics].mean().reset_index()
ireland_trend = df4.groupby('year_created')[fair_metrics].mean().reset_index()
In [128]:
import matplotlib.pyplot as plt

# Function to plot trends
def plot_trends(country_trend, country_name):
    plt.figure(figsize=(14, 8))
    for metric in fair_metrics:
        plt.plot(country_trend['year_created'], country_trend[metric], label=metric)
    plt.title(f'Trend of FAIR Scores Over Time in {country_name}')
    plt.xlabel('Year')
    plt.ylabel('Average FAIR Score')
    plt.legend(title='FAIR Metric', bbox_to_anchor=(1.05, 1), loc='upper left')
    plt.grid(True)
    plt.show()

# Plot trends for each country
#plot_trends(austria_trend, 'Austria')
plot_trends(germany_trend, 'Germany')
plot_trends(switzerland_trend, 'Switzerland')
plot_trends(ireland_trend, 'Ireland')

Making the trendlines a bit smoother.

In [197]:
from scipy.interpolate import make_interp_spline

# Function to plot trends with smoother lines
def plot_trends(country_trend, country_name):
    plt.figure(figsize=(14, 8))
    x = country_trend['year_created']
    
    for metric in fair_metrics:
        y = country_trend[metric]
        X_Y_Spline = make_interp_spline(x, y)
        
        # Generate more x values to create a smooth curve
        X_ = np.linspace(min(x), max(x), 500)
        Y_ = X_Y_Spline(X_)
        
        plt.plot(X_, Y_, label=metric)
    
    plt.title(f'Trend of FAIR Scores Over Time in {country_name}')
    plt.xlabel('Year')
    plt.ylabel('Average FAIR Score')
    plt.legend(title='FAIR Metric', bbox_to_anchor=(1.05, 1), loc='upper left')
    plt.grid(True)
    plt.show()

# Plot trends for each country
# plot_trends(austria_trend, 'Austria')
plot_trends(germany_trend, 'Germany')
plot_trends(switzerland_trend, 'Switzerland')
plot_trends(ireland_trend, 'Ireland')

Trend average (equally weighted for the 3 countries that have "created" column)¶

In [209]:
# Function to calculate average and plot trend
def plot_average_trend(countries):
    plt.figure(figsize=(14, 8))
    years = countries[0]['year_created']
    metrics = fair_metrics
    
    for idx, metric in enumerate(metrics):
        # Calculate average for each metric across all countries
        avg_scores = np.mean([country[metric] for country in countries], axis=0)
        
        # Smoothing the line
        X_Y_Spline = make_interp_spline(years, avg_scores)
        X_ = np.linspace(min(years), max(years), 500)
        Y_ = X_Y_Spline(X_)
        
        # Plotting
        plt.plot(X_, Y_, label=metric)
    
    plt.title('Average Trend of FAIR Scores Over Time Across Countries')
    plt.xlabel('Year')
    plt.ylabel('Average FAIR Score')
    plt.legend(title='FAIR Metric')
    plt.grid(True)
    plt.show()

# List of country data
countries = [germany_trend[1:], switzerland_trend[4:], ireland_trend[1:]]

# Plot the average trends
plot_average_trend(countries)

Conclusion:¶

No visible improvements over the last years for any country - if anything, there's a small downwards trend from 2022.

In [125]:
def plot_heatmap(country_trend, country_name):
    plt.figure(figsize=(14, 8))
    sns.heatmap(country_trend.set_index('year_created')[fair_metrics].T, annot=True, cmap="YlGnBu")
    plt.title(f'Heatmap of FAIR Scores Over Time in {country_name}')
    plt.xlabel('Year')
    plt.ylabel('FAIR Metrics')
    plt.show()

# Plot heatmaps for each country
#plot_heatmap(austria_trend, 'Austria')
plot_heatmap(germany_trend, 'Germany')
plot_heatmap(switzerland_trend, 'Switzerland')
plot_heatmap(ireland_trend, 'Ireland')

The heatmaps confirm the findings of the trendlines.

Comparative analysis¶

In [129]:
# Aggregate average FAIR scores for each country
mean_scores = {
    'Austria': df[fair_metrics].mean(),
    'Germany': df2[fair_metrics].mean(),
    'Switzerland': df3[fair_metrics].mean(),
    'Ireland': df4[fair_metrics].mean()
}

mean_scores_df = pd.DataFrame(mean_scores).T

# Create a stacked bar chart
mean_scores_df.plot(kind='bar', stacked=True, figsize=(14, 8))
plt.title('Average FAIR Scores by Country')
plt.xlabel('Country')
plt.ylabel('Average Score')
plt.legend(title='FAIR Metrics', bbox_to_anchor=(1.05, 1), loc='upper left')
plt.show()
In [136]:
fair_metrics
Out[136]:
['F1A',
 'F1B',
 'F2A',
 'F2B',
 'A1.1',
 'A1.2',
 'I1',
 'I2',
 'I3',
 'R1.1',
 'R1.2',
 'R1.3']
In [211]:
from math import pi

# Function to create radar chart
def plot_radar_chart(data, country_name):
    # Number of variables we're plotting
    categories = fair_metrics
    N = len(categories)

    # What will be the angle of each axis in the plot? (we divide the plot / number of variables)
    angles = [n / float(N) * 2 * pi for n in range(N)]
    angles += angles[:1]

    # Initialise the radar plot
    ax = plt.subplot(111, polar=True)

    # If you want the first axis to be on top:
    ax.set_theta_offset(pi / 2)
    ax.set_theta_direction(-1)

    # Draw one axe per variable + add labels
    plt.xticks(angles[:-1], categories)

    ax.set_yticklabels([])

    # Draw ylabels
    ax.set_rlabel_position(0)
    plt.yticks([0.2, 0.4, 0.6, 0.8, 1], color="grey", size=7)
    plt.ylim(0, max_score)

    # Ind1
    values = data.values.flatten().tolist()
    values += values[:1]
    ax.plot(angles, values, linewidth=1, linestyle='solid', label=country_name)
    ax.fill(angles, values, alpha=0.1)

    # Add a title
    plt.title(f'Radar Chart of the scores per Principles', size=20, color='black', y=1.1)

# Prepare data for radar chart
mean_scores_radar = {
    'Austria': df[fair_metrics].mean(),
    'Germany': df2[fair_metrics].mean(),
    'Switzerland': df3[fair_metrics].mean(),
    'Ireland': df4[fair_metrics].mean()
}

max_score = 2

# Plot radar charts for each country
plt.figure(figsize=(10, 10))
plot_radar_chart(mean_scores_radar['Austria'], 'Austria')
plot_radar_chart(mean_scores_radar['Germany'], 'Germany')
plot_radar_chart(mean_scores_radar['Switzerland'], 'Switzerland')
plot_radar_chart(mean_scores_radar['Ireland'], 'Ireland')
plt.legend(loc='upper right', bbox_to_anchor=(1.3, 1.1))
plt.show()

Overall similar "strengths and weaknesses", but the data frm the Austrian portal tends to always have slightly higher scores.

F1B and I3 metrics are (virtually) always 0.

License types, languages¶

Not designed as no meaningful results came out.

In [171]:
import matplotlib.pyplot as plt

# Function to plot pie charts for categorical data
def plot_pie_chart(data, column, country_name):
    plt.figure(figsize=(8, 8))
    data[column].value_counts().plot.pie(autopct='%1.1f%%', startangle=140, colors=sns.color_palette("pastel"))
    plt.title(f'Distribution of {column} in {country_name}')
    plt.ylabel('')
    plt.show()

# Plot pie charts for each country and a specific column, e.g., 'license_id'
plot_pie_chart(df, 'license', 'Austria')
#plot_pie_chart(df2, 'license_id', 'Germany')
#plot_pie_chart(df3, 'license_id', 'Switzerland')
plot_pie_chart(df4, 'license_title', 'Ireland')


plot_pie_chart(df, 'schema_language', 'Austria')
#plot_pie_chart(df2, 'language', 'Germany')
plot_pie_chart(df3, 'language', 'Switzerland')
plot_pie_chart(df4, 'language', 'Ireland')
In [177]:
len(df4)
Out[177]:
13090

Distribution of scores by country¶

Interestingly the overwhelming majority of Austrian and Irish datasets have almost identical scores, while the German and Swiss datasets take a much wider range.

In [178]:
# Create the boxplot
plt.figure(figsize=(12, 8))
sns.boxplot(x='Country', y='Score', data=combined_df)
plt.title('Boxplot of Scores by Country')
plt.xticks(rotation=90) # Rotate country names if they are long
plt.xlabel('Country')
plt.ylabel('Score')
plt.show()

Interpreting some findings¶

  • A look into why some visualizations look this way

Is it because they are published by the same people/organization?¶

In [228]:
display(df.publisher.value_counts())
#display(df.maintainer.value_counts())

Offenerhaushalt.at               6965
Nationalparks Austria            3531
Umweltbundesamt GmbH              791
Statistik Austria                 394
Land Vorarlberg                   379
                                 ... 
AEP Planung und Beratung GmbH       1
Monopolverwaltung GmbH              1
ASFINAG                             1
Stadtgemeinde Trofaiach             1
Gebetsberger ZT GmbH                1
Name: publisher, Length: 65, dtype: int64
In [229]:
display(df2.author.value_counts())
#display(df2.maintainer.value_counts())

Institut für Hygiene und Umwelt                                                                 3031
Landesbetrieb Geoinformation und Vermessung                                                     2895
Statistisches Amt für Hamburg und Schleswig-Holstein                                            2794
Hanse- und Universitätsstadt Rostock – Kataster-, Vermessungs- und Liegenschaftsamt              110
Behörde für Umwelt, Klima, Energie und Agrarwirtschaft (BUKEA)                                    61
Hanse- und Universitätsstadt Rostock – Hauptamt                                                   51
Hanse- und Universitätsstadt Rostock – Amt für Umwelt- und Klimaschutz                            33
Hanse- und Universitätsstadt Rostock – Stadtamt                                                   13
Behörde für Stadtentwicklung und Wohnen (BSW)                                                     10
Hanse- und Universitätsstadt Rostock – Amt für Stadtgrün, Naturschutz und Friedhofswesen           8
Hanse- und Universitätsstadt Rostock – Tiefbauamt                                                  6
Steinbeis-Transferzentrum Geoinformatik                                                            5
Tourismuszentrale Rostock und Warnemünde                                                           5
Hanse- und Universitätsstadt Rostock – Amt für Stadtentwicklung, Stadtplanung und Mobilität        5
Hanse- und Universitätsstadt Rostock – Amt für Kultur, Denkmalpflege und Museen                    3
Stadtwerke Rostock AG                                                                              3
Landesamt für Innere Verwaltung Mecklenburg-Vorpommern                                             3
Hanse- und Universitätsstadt Rostock – Stadtforstamt                                               3
Hanse- und Universitätsstadt Rostock – Brandschutz- und Rettungsamt                                2
Hanse- und Universitätsstadt Rostock – Amt für Soziales und Teilhabe                               2
Hanse- und Universitätsstadt Rostock – Hafen- und Seemannsamt                                      2
Rostocker Freizeitzentrum e. V.                                                                    2
Hanse- und Universitätsstadt Rostock – Amt für Stadtentwicklung, Stadtplanung und Wirtschaft       2
Statistisches Amt für Hamburg und Schleswig-Holstein, Vertrieb des Statistikamts Nord              2
Hanse- und Universitätsstadt Rostock – Amt für Umweltschutz                                        1
Bezirksämter                                                                                       1
Kulturbehörde                                                                                      1
Staatliches Amt für Landwirtschaft und Umwelt Mittleres Mecklenburg                                1
Friedrich Wilhelm Carl Graf von Schmettau                                                          1
Statistisches Amt für Hamburg und Schleswig-Holstein, Webangebote                                  1
Behörde für Umwelt und Energie (BUE), Amt für Umweltschutz, Geologisches Landesamt                 1
Carl Friedrich von Wiebeking                                                                       1
Max-Samuel-Haus – Stiftung Begegnungsstätte für jüdische Geschichte und Kultur in Rostock          1
Julius Michael Tarnow                                                                              1
Hanse- und Universitätsstadt Rostock – Bauamt                                                      1
OpenStreetMap                                                                                      1
Eine-Welt-Landesnetzwerk Mecklenburg-Vorpommern e. V.                                              1
Hanse- und Universitätsstadt Rostock – Büro der Oberbürgermeisterin                                1
Landesbetrieb Immobilienmanagement und Grundvermögen                                               1
Name: author, dtype: int64
In [236]:
#display(df3.author.value_counts())
display(df3.owner_org.value_counts())

4309c328-c618-4077-bcdc-378a21ee1b46    2917
aa742a0e-7ba4-4f65-ab9f-c587dec73891     627
1172ac79-c506-44cc-9463-ae60d96f09f8     593
6c1b8763-a767-40a4-993c-fc515db2e28c     583
8bedecea-4a19-4807-8d58-59b83b4a6f92     505
                                        ... 
0140936c-d4f9-4f5d-a6cb-929b90257cda       1
55e35d07-db62-4e97-89b3-c08d82b7ee68       1
645e9975-f9d4-48c7-b1c2-342fe6f9c94e       1
a4136229-3dc5-4353-947f-bcfdf8995735       1
fa7cf5ee-1353-4182-b679-4101afe66046       1
Name: owner_org, Length: 107, dtype: int64
In [238]:
display(df4.author.value_counts())

Fingal County Council                                   307
Open Data Liaison Officer                               274
Helena Campbell                                         263
South Dublin County Council                             155
Dublin City Council                                      99
Deirdre Kelly                                            75
Geological Survey of Ireland                             45
Environmental Protection Agency                          44
DLR GIS Officer                                          41
National Biodiversity Data Centre                        29
Dún Laoghaire-Rathdown County Council                    28
Not supplied                                             11
Data and Analytics Unit                                  10
Data Officer                                              7
Roscommon County Council                                  7
Data & Analytics Unit                                     5
Wexford County Council                                    4
Department of Arts, Heritage and the Gaeltacht            3
Donal McWeeney                                            3
DCC Transport City Centre Projects                        2
Smart Dublin Team                                         2
info@smartdublin.ie                                       2
Smart Dublin                                              2
Oliver Dawkins                                            1
K Cooney                                                  1
Nicola Graham                                             1
Dublin City Council                                       1
Richard Sheehan                                           1
Dublin City Council Culture Company                       1
DCC                                                       1
L Binns                                                   1
The Property Registration Authority                       1
GIS Officer                                               1
Sid Daly                                                  1
Meath County Council                                      1
donna gartland                                            1
CSO                                                       1
data@dublincity.ie                                        1
Department of Housing, Local Government and Heritage      1
Google, Aclima & Dublin City Council                      1
National Transport Authority                              1
DCC Air Quality Monitoring and Noise Control Unit         1
Name: author, dtype: int64
In [231]:
df3.head()
Out[231]:
Unnamed: 0 owner_org maintainer issued title_for_slug qualified_relations private maintainer_email num_tags contact_points keywords temporals id metadata_created conforms_to metadata_modified author author_email relations state version relationships_as_object license_id type resources scheduled num_resources description tags title language groups creator_user_id display_name relationships_as_subject license_title publisher organization name isopen accrual_periodicity documentation modified url see_alsos revision_id identifier spatial coverage extras FAIR_assessment year_created year_modified F1A F1B F2A F2B A1.1 A1.2 I1 I2 I3 R1.1 R1.2 R1.3 Score Country
0 0 b3d5c5d8-f736-459e-b23b-fe0ea6196cf2 Sara Schärrer 2023-03-02T00:00:00 Tierseuchenmeldungen in der Schweiz [] False sara.schaerrer@blv.admin.ch 9 [{'name': 'Kompetenzzentrum Daten', 'email': '... {'fr': ['animal', 'veterinaire', 'epizootie'],... [{'start_date': '1991-01-01T00:00:00', 'end_da... 702ddd5c-ca98-4a78-96ef-9aafdbea8f2d 2023-05-05 06:30:17.188021 [] 2024-06-19 07:29:52.862215 NaN NaN [{'url': 'https://www.fedlex.admin.ch/eli/cc/1... active NaN [] NaN dataset [{'documentation': [], 'url_type': None, 'cach... NaN 3 {'fr': "La banque de données InfoSM (= système... [{'vocabulary_id': None, 'state': 'active', 'd... {'fr': 'Annonces des cas d‘épizootie en Suisse... ['de', 'fr', 'it'] [{'display_name': {'fr': 'Santé', 'de': 'Gesun... 0594d621-c92a-4a32-809c-98e281dc7944 {} [] NaN {"url": "https://www.blv.admin.ch", "name": "B... {'display_name': {'fr': 'Office fédéral de la ... meldepflichtige-tierseuchen-in-der-schweiz False http://publications.europa.eu/resource/authori... [] NaN https://infosm.blv.admin.ch/ [] c8793a79-b0ff-4e59-98bc-0dec70e90e88 tierseuchen@bundesamt-fur-lebensmittelsicherhe... NaN NaN NaN [{'metric': 'F1A', 'score': '2'}, {'metric': '... 2023 2024 2 0 1 1 2 0 1 1 0 0 0 1 9 Switzerland
1 25 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2023-08-30T00:00:00 klimaanalyse-windgeschwindigkeit-nacht-zukunft... [] False NaN 0 [{'email': 'afu@bd.so.ch', 'name': 'afu@bd.so.... {'fr': [], 'de': ['klimaanalyse'], 'en': [], '... [] d2b483d0-838c-4a3b-b93e-16685da0b0f0 2024-06-19 05:37:57.613414 [] 2024-06-19 05:37:57.613423 NaN NaN [{'url': 'https://www.geocat.ch/geonetwork/srv... active NaN [] NaN dataset [{'protocol': 'WWW:DOWNLOAD:GeoTIFF', 'cache_l... NaN 1 {'fr': '', 'de': 'Windgeschwindigkeit um 4 Uhr... [] {'fr': '', 'de': 'Klimaanalyse - Windgeschwind... ['de'] [{'display_name': {'fr': 'Régions et villes', ... af084126-f711-4016-a585-70354e997796 {} [] NaN {"url": "https://afu.so.ch", "name": "Amt f\u0... {'display_name': {'fr': '', 'de': 'Kanton Solo... klimaanalyse-windgeschwindigkeit-nacht-zukunft... False http://publications.europa.eu/resource/authori... NaN 2023-08-30T00:00:00 https://data.geo.so.ch?filter=ch.so.afu.klimaa... [] 5d7ec063-fd29-4322-9c33-04bc607e72c1 f5312f7d-dba5-4ca3-8419-de3fea19d6c7@kanton_so... Kanton Solothurn (SO) NaN [{'key': 'harvest_object_id', 'value': '230dfa... [{'metric': 'F1A', 'score': '2'}, {'metric': '... 2024 2024 2 0 1 1 2 0 1 1 0 0 0 1 9 Switzerland
2 26 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2024-06-06T00:00:00 gebaudeflachen-mit-adressen-amtliche-vermessung2 [] False NaN 0 [{'email': 'agi@bd.so.ch', 'name': 'agi@bd.so.... {'fr': [], 'de': ['gebaude', 'adresse', 'egid'... [] 0f3681b9-7508-421f-8f23-f482de9a8485 2024-06-19 05:34:29.756561 [] 2024-06-19 05:34:29.756569 NaN NaN [{'url': 'https://www.geocat.ch/geonetwork/srv... active NaN [] NaN dataset [{'protocol': 'WWW:DOWNLOAD:DXF', 'cache_last_... NaN 4 {'fr': '', 'de': 'Gebäudeflächen und Adressen ... [] {'fr': '', 'de': 'Gebäudeflächen mit Adressen ... ['de'] [{'display_name': {'fr': 'Régions et villes', ... af084126-f711-4016-a585-70354e997796 {} [] NaN {"url": "https://agi.so.ch", "name": "Amt f\u0... {'display_name': {'fr': '', 'de': 'Kanton Solo... gebaudeflachen-mit-adressen-amtliche-vermessung2 False http://publications.europa.eu/resource/authori... NaN 2024-06-06T00:00:00 https://data.geo.so.ch?filter=ch.so.agi.av.geb... [] cd1c1574-bab5-46a7-a55b-13d026cc857c 1e984455-1980-4ba0-b87c-633b0971d99d@kanton_so... Kanton Solothurn (SO) NaN [{'key': 'harvest_object_id', 'value': '10425c... [{'metric': 'F1A', 'score': '2'}, {'metric': '... 2024 2024 2 0 1 1 2 0 1 1 0 0 0 1 9 Switzerland
3 27 1e4cc1ec-05f2-4801-821e-492a8199dced NaN 2024-01-31T00:00:00 kantonsstrassen1 [] False NaN 0 [{'email': 'avt@bd.so.ch', 'name': 'avt@bd.so.... {'fr': [], 'de': ['strasse', 'strassen', 'verk... [] fa77acc4-d44d-455b-a4b1-42c06824ff33 2024-06-19 05:33:30.010765 [] 2024-06-19 05:33:30.010773 NaN NaN [{'url': 'https://www.geocat.ch/geonetwork/srv... active NaN [] NaN dataset [{'protocol': 'WWW:DOWNLOAD:DXF', 'cache_last_... NaN 4 {'fr': '', 'de': 'Die Kantonsstrassen sind übe... [] {'fr': '', 'de': 'Kantonsstrassen', 'en': '', ... ['de'] [{'display_name': {'fr': 'Régions et villes', ... af084126-f711-4016-a585-70354e997796 {} [] NaN {"url": "https://avt.so.ch", "name": "Amt f\u0... {'display_name': {'fr': '', 'de': 'Kanton Solo... kantonsstrassen1 False http://publications.europa.eu/resource/authori... ['https://geo.so.ch/map?l=ch.so.avt.kantonsstr... 2024-01-31T00:00:00 https://data.geo.so.ch?filter=ch.so.avt.kanton... [] 4491b576-5897-4ea9-9f89-fcc121cec591 9209c1e4-6adf-452b-9870-235a6dbe01bb@kanton_so... Kanton Solothurn (SO) NaN [{'key': 'harvest_object_id', 'value': 'd4e9a6... [{'metric': 'F1A', 'score': '2'}, {'metric': '... 2024 2024 2 0 1 1 2 0 1 1 0 0 0 1 9 Switzerland
4 29 51ffc9d5-bf18-4bb4-9b26-ad9b45a60e14 Stadt Winterthur, Stadtentwicklung der Stadt W... 2022-09-20T17:10:34+01:00 beschaftigtendichte-in-der-stadt-winterthur-ab... [] False statistik@win.ch 4 [{'email': 'statistik@win.ch', 'name': 'Stadt ... {'fr': [], 'de': ['bevoelkerung', 'gemeinden',... [{'start_date': '1995-01-01T00:00:00', 'end_da... 86419980-f6af-462a-a672-27ed54f77299 2022-11-15 16:04:52.337827 [] 2024-06-19 04:12:54.928353 NaN NaN [{'url': 'https://openzh.github.io/starter-cod... active NaN [] NaN dataset [{'byte_size': 10925, 'cache_last_updated': No... NaN 2 {'fr': '', 'de': 'Anzahl und Anteil Beschäftig... [{'vocabulary_id': None, 'state': 'active', 'd... {'fr': '', 'de': 'Beschäftigtendichte in der S... ['de'] [] af084126-f711-4016-a585-70354e997796 {} [] NaN {"url": "https://opendata.swiss/organization/s... {'display_name': {'fr': '', 'de': 'Stadt Winte... beschaftigtendichte-in-der-stadt-winterthur-ab... False http://publications.europa.eu/resource/authori... [] 2024-06-04T16:54:18+01:00 https://stadt.winterthur.ch/themen/die-stadt/w... [{'dataset_identifier': '1521@stadt-winterthur... 449f19b9-7b35-4d6e-beeb-2f3492da35e6 1621@stadt-winterthur NaN NaN [{'value': '1621@stadt-winterthur', 'key': 'gu... [{'metric': 'F1A', 'score': '2'}, {'metric': '... 2022 2024 2 0 1 1 2 0 1 1 0 0 0 1 9 Switzerland

Lets look a bit more into the different values they take to confirm our findings from the boxplots¶

In [216]:
combined_df.Score.value_counts()
Out[216]:
4.0     17194
15.0    10849
9.0      6537
11.0     3851
5.0      3064
12.0     2800
16.0      719
21.0      303
24.0      280
19.0      264
0.0       169
17.0      145
13.0      144
2.0       103
7.0        30
6.0        29
8.0        24
20.0       23
10.0       15
22.0        6
14.0        4
18.0        4
1.0         1
3.0         1
Name: Score, dtype: int64
In [187]:
combined_df[fair_metrics].value_counts()
Out[187]:
F1A  F1B  F2A  F2B  A1.1  A1.2  I1  I2  I3  R1.1  R1.2  R1.3
2    0    0    0.0  2.0   0     0   0   0   0     0     0       17194
          1    1.0  2.0   2     1   1   0   2     2     1       10752
                          0     1   1   0   0     0     1        6531
                                                  2     1        3249
0    0    1    1.0  0.0   0     1   1   0   0     0     1        3051
                                                                ...  
                                                  2     1           1
                                        2   0     2     1           1
2    0    1    2.0  2.0   0     1   2   2   0     0     2           1
               1.0  2.0   2     1   1   0   2     2     0           1
               2.0  2.0   0     1   1   0   0     0     2           1
Length: 71, dtype: int64
In [181]:
df[fair_metrics].value_counts()
Out[181]:
F1A  F1B  F2A  F2B  A1.1  A1.2  I1  I2  I3  R1.1  R1.2  R1.3
2    0    1    1    2     2     1   1   0   2     2     1       10724
                          0     1   1   0   0     2     1        3243
0    0    0    0    0     0     0   0   0   0     0     0          74
2    0    1    1    2     2     1   1   2   2     2     1          44
     2    1    1    2     2     1   1   2   2     2     1          40
                          0     1   1   0   0     2     1           7
     1    1    1    2     2     1   1   2   2     2     1           2
     0    2    1    2     2     2   1   2   2     2     1           1
     1    1    1    2     2     1   1   0   2     2     1           1
     2    1    1    2     0     1   1   2   0     2     1           1
dtype: int64

99% of the Austrian datasets can be described by two score-combinations (out of the multiple thousand potential ones), which suggests there's a strong framework/recommendation how these datasets should be published. (In total 10 unique combinations)

In [183]:
df4[fair_metrics].value_counts()
Out[183]:
F1A  F1B  F2A  F2B  A1.1  A1.2  I1  I2  I3  R1.1  R1.2  R1.3
2    0    0    0.0  2.0   0     0   0   0   0     0     0       10428
          1    2.0  2.0   0     1   2   0   0     0     2        1331
               1.0  2.0   0     1   1   0   0     0     1        1008
0    0    0    0.0  2.0   0     0   0   0   0     0     0          71
                    0.0   0     0   0   0   0     0     0          38
2    0    1    1.0  2.0   2     1   1   0   2     2     1          28
     2    1    1.0  2.0   2     1   1   2   2     2     1          14
     0    1    1.0  2.0   0     0   0   0   0     0     0          12
          0    0.0  2.0   0     0   0   0   0     0     1          10
          1    1.0  2.0   0     1   2   0   0     0     2          10
          0    0.0  2.0   0     1   1   0   0     0     1          10
     2    1    1.0  2.0   0     1   1   2   0     0     1           9
0    0    1    2.0  0.0   0     1   2   0   0     0     2           9
2    0    1    1.0  2.0   0     1   1   0   0     0     2           9
               2.0  2.0   0     1   2   0   0     0     1           8
0    0    1    1.0  0.0   2     1   1   0   2     2     1           7
2    0    1    2.0  2.0   0     0   0   0   0     0     0           7
               1.0  2.0   2     1   1   2   2     2     1           6
                          0     1   1   0   0     0     0           5
0    0    1    1.0  0.0   0     1   1   0   0     0     1           5
2    0    0    0.0  2.0   0     0   0   0   0     0     2           5
          1    0.0  2.0   0     1   0   0   0     0     0           4
          0    0.0  2.0   0     1   2   0   0     0     2           4
     2    1    2.0  2.0   2     1   2   2   2     2     2           3
     0    1    1.0  2.0   0     1   1   2   0     0     1           3
                                        0   2     2     1           2
     2    1    1.0  2.0   0     1   1   2   0     2     1           2
               0.0  2.0   0     0   0   0   0     0     0           2
0    0    1    1.0  2.0   0     1   1   2   0     2     1           2
2    0    1    1.0  2.0   0     1   1   2   0     2     1           2
          0    2.0  2.0   0     1   2   0   0     0     2           2
          1    1.0  2.0   2     1   1   2   2     0     1           2
               0.0  2.0   0     0   0   0   0     0     0           2
                                1   1   0   0     0     1           2
          0    1.0  2.0   0     1   1   0   0     0     1           2
               0.0  2.0   0     0   1   0   0     0     1           2
0    0    1    1.0  0.0   0     1   1   2   0     2     1           1
2    2    1    2.0  2.0   0     1   1   2   0     0     1           1
0    0    0    0.0  0.0   0     0   1   0   0     0     1           1
2    2    1    1.0  2.0   0     1   1   0   0     0     1           1
               0.0  2.0   0     1   1   0   0     0     1           1
     0    1    1.0  0.0   0     1   1   2   0     2     1           1
     1    1    0.0  2.0   0     0   0   0   0     0     0           1
     0    1    2.0  2.0   2     1   2   2   2     2     2           1
               1.0  2.0   0     1   1   0   0     2     1           1
               2.0  2.0   0     1   2   0   0     0     0           1
                                    1   0   0     0     2           1
                                                        1           1
0    0    1    1.0  2.0   0     1   1   0   0     2     1           1
2    0    1    0.0  0.0   0     0   0   0   0     0     0           1
0    0    1    2.0  2.0   0     1   2   0   0     0     2           1
2    0    1    1.0  2.0   2     1   1   0   2     2     0           1
                          0     1   0   0   0     0     0           1
0    0    0    0.0  0.0   0     0   0   0   0     0     1           1
          1    1.0  2.0   0     1   1   0   0     0     1           1
dtype: int64

Also for Ireland the majority of datasets only differ in very few principles, but there are a couple that take also different combinations.

In [185]:
df2[fair_metrics].value_counts()
Out[185]:
F1A  F1B  F2A  F2B  A1.1  A1.2  I1  I2  I3  R1.1  R1.2  R1.3
0    0    1    1    0     0     1   1   0   0     0     1       3035
2    0    1    1    2     0     1   1   0   0     0     1       2963
          0    0    2     0     0   0   0   0     0     0       2805
          1    2    2     2     1   2   0   0     2     2        268
0    0    0    0    0     0     0   0   0   0     0     0          3
dtype: int64
In [186]:
df3[fair_metrics].value_counts()
Out[186]:
F1A  F1B  F2A  F2B  A1.1  A1.2  I1  I2  I3  R1.1  R1.2  R1.3
2    0    0    0    2     0     0   0   0   0     0     0       3961
          1    1    2     0     1   1   0   0     0     1       2560
               2    2     0     1   2   0   0     0     2       1469
               1    2     0     1   1   2   0     0     1        567
               2    2     2     1   2   0   2     0     2        434
     2    2    1    2     2     2   1   2   2     2     1        303
               2    2     2     2   2   2   2     2     2        280
          1    1    2     2     1   1   2   2     2     1        209
     0    1    1    2     0     1   1   2   0     2     1        121
                          2     1   1   2   2     0     1         90
                                                  2     1         57
0    0    0    0    0     0     0   0   0   0     0     0         54
2    2    1    1    2     2     1   1   2   2     0     1         33
0    0    0    0    2     0     0   0   0   0     0     0         31
2    0    1    2    2     2     1   2   2   2     2     2         22
     2    1    2    2     0     1   2   0   0     2     2         16
0    0    1    1    0     0     1   1   0   0     0     1         11
                    2     0     1   1   0   0     0     1          6
2    0    1    1    2     0     1   1   0   0     2     1          5
0    0    1    2    0     0     1   2   0   0     0     2          5
2    1    1    2    2     2     1   2   0   2     0     2          5
     0    1    0    2     0     1   0   0   0     0     0          4
     2    1    2    2     2     1   2   2   2     2     2          3
               1    2     0     1   1   2   0     0     1          3
     0    1    2    2     2     1   2   2   2     0     2          2
     2    1    1    2     2     1   1   0   0     2     1          1
                          0     1   1   2   0     2     1          1
               2    2     0     1   2   0   0     0     2          1
     0    0    0    2     0     0   0   0   0     0     2          1
0    0    1    1    0     0     1   1   0   0     2     1          1
2    0    1    2    2     0     1   2   2   0     0     2          1
0    0    1    2    2     0     1   2   0   0     0     2          1
dtype: int64

Trying out some visualizations¶

In [190]:
df[fair_metrics].hist(bins=20, figsize=(20, 15), layout=(4, 3))
plt.suptitle('Histograms of FAIR principle scores Austria')
plt.show()
In [195]:
for i in fair_metrics:
    display(combined_df[i].value_counts())

2    43200
0     3359
Name: F1A, dtype: int64
0    45617
2      933
1        9
Name: F1B, dtype: int64
1    28472
0    17503
2      584
Name: F2A, dtype: int64
1.0    25165
0.0    17516
2.0     3873
Name: F2B, dtype: int64
2.0    43311
0.0     3247
Name: A1.1, dtype: int64
0    33978
2    12581
Name: A1.2, dtype: int64
1    28462
0    17513
2      584
Name: I1, dtype: int64
1    25161
0    17519
2     3879
Name: I2, dtype: int64
0    44732
2     1827
Name: I3, dtype: int64
0    34245
2    12314
Name: R1.1, dtype: int64
0    31137
2    15422
Name: R1.2, dtype: int64
1    25164
0    17509
2     3886
Name: R1.3, dtype: int64
In [191]:
combined_df[fair_metrics].hist(bins=20, figsize=(20, 15), layout=(4, 3))
plt.suptitle('Histograms of FAIR principle scores all countries')
plt.show()

Observations¶

  • F1A(Unique IDs - FAIRChecker check that the resource identifier is an URL that can be reach, meaning it is unique, it is even better if the URL refer to a DOI) is the only principle that almost all datasets comply with - although its not surprising, as we used the URLs to perform the assessment

  • F1B(Persistent IDs - Weak : FAIR-Checker verifies that at least one namespace from identifiers.org is used in metadata. Strong : FAIR-Checker verifies that the “identifier” property from DCTerms or Schema.org vocabularies is present in metadata.) and I3(External links - FAIR-Checker verifies that at least 3 different URL authorities are used in the URIs of RDF metadata.) principles on the other hand are basically never adhered